| Gary Marcus' tone amounts to base trolling and it's a shame that's how he
chooses to carry out his criticism. I understand your frustration. Regarding the lack of transfer, yes, AlphaGo, AlphaZero and most of their
variants have boards of fixed size and shape hard-coded in their architecture
(as they have the types of piece moves-hard coded) and need architectural
modifications and re-training before they can play on different boards or with
different pieces (e.g. AlphaGo can't play Chess and Shoggi unmodified). The
KataGo paper (the paper you linked) is one exception to this. Personally, I
don't know others. Anyway general game-playing is a hard task and nobody
claims it's solved by AlphaGo. Regarding KataGo its main contribution is a significant reduction to the cost
of training an AlpahGo variant while maintaining a competitive performance.
This is very promising- after DeepBlue, creating a chess engine became cheaper
and cheaper until they could run on a smartphone. We are far from that with
Go computer players. However, in the KataGo paper, major gains are claimed to come from a)
game-playing specific or MCTS-specific improvements (playout cap
randomisation, forced playouts and policy target pruning) or
architecture-specific improvements (global pooling) or, b) domain-specific improvements
(auxiliary ownership and score targets). Finally, KataGo has a few
game-specific features (liberties, pass-alive regions and ladder features). The KataGo paper itself says it very clearly. I quote, snipping for brevity: Second, our work serves as a case study that there is still a significant
efficiency gap between AlphaZero's methods and what is possible from
self-play. We find nontrivial further gains from some domain-specific methods
(...) We also find that a set of standard game-specific input features still
significantly accelerates learning, showing that AlphaZero does not yet
obsolete even simple additional tuning. Finally, "it would obviously work so nobody tried" would make sense if it
wasn't for the extremely competitive nature of machine learning research where
every novel result is presented as a big breakthrough. Also, if something is
obvious but never seems to make it to publication the chances are someone has
tried and it didn't work as expected so they shelved the paper. We all know
what happens to negative results in machine learning. |
And my point is that KataGo shows that if you make the relatively minor architectural changes necessary to do this at all, it works just fine. None of the other tweaks it makes, useful as they may be, have anything to do with fixing transfer learning, because there's nothing to fix. It's a pretty absurd claim to claim that a CNN which works fine on 19x19 will suddenly collapse and show no transfer on, say, 17x17, and KataGo demonstrates that this does not happen.
> Also, if something is obvious but never seems to make it to publication the chances are someone has tried and it didn't work as expected so they shelved the paper.
'What if Go but rectangular boards' is pretty dumb when you have chess and shogi and other domains showing that A0 works, so I feel confident that no one like DM seriously tried and simply buried their failures. (Publication bias requires there to be a literature that can be differentially published, and competition presumes the existence of >0 entities competing; there is no active field of 'rectangular Go'.)